Introduction
The Amazon rain forest is of particular relevance in Earth system science.
It represents a vulnerable ecosystem of global importance, which is
increasingly disturbed by the combination of climate change and agricultural
as well as infrastructural expansion (Davidson et al., 2012). In fact, the
Amazon has been ranked as one of the potential tipping points in the global
climate system (Lenton et al., 2008). Furthermore, it represents a unique
location to study the human influence on the atmosphere as it represents one
of the few remaining continental places where episodes with a near-pristine
atmospheric state can be found (Andreae, 2007; Martin et al., 2010a;
Pöschl et al., 2010).
Composite maps combining backward trajectory ensembles with satellite
data products contrasting wet and dry conditions in Amazonia. Multi-year time
periods from 2008 to 2016 were averaged to retrieve representative results.
All wet season data shown here represent averages of the months February–May
and the dry season data represent averages of the months August–November.
Backward trajectory ensembles are shown as contour plots with contour lines
representing fraction of occurrence of overpassing trajectories in a certain
region as described in Pöhlker et al. (2018) (see legend in a).
(a, b) MODIS-derived cloud top temperature data showing location of
ITCZ belt with deep convective clouds and corresponding cold cloud tops.
(c, d) TRMM-derived precipitation rate showing regions where strong
rain-related aerosol scavenging is expected. The belt with strongest
precipitation rates is collocated with the ITCZ. (e, f) MODIS-derived aerosol optical depth (AOD) illustrating the dominant
aerosol sources in Africa and South America relevant for the ATTO site
measurements. (g, h) AIRS-derived CO maps illustrating hot spots of
biomass burning with strong CO emissions.
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Since the late 1980s, numerous field campaigns have been conducted in the
Amazon region, which focused on specific aspects of the complex atmospheric
cycling for time periods of weeks, months, and in some cases up to years
(e.g., Andreae et al., 1988, 2004, 2015; Talbot et al., 1988, 1990; Harriss
et al., 1990; Artaxo et al., 1993, 2013b; Martin et al., 2010a, 2016; Brito
et al., 2014; Wendisch et al., 2016). In 2010/11, the Amazon tall tower
observatory (ATTO) has been established ∼ 150 km northeast (NE) of the
city of Manaus, Brazil, for continuous and detailed observation of
meteorology, trace gases, aerosols, and ecology in order to study long-term
trends of the Amazonian hydrological and biogeochemical cycling in relation
to the increasing extent of man-made perturbations (Andreae et al., 2015).
Generally speaking, the central Amazonian atmosphere swings back and forth
between a mostly clean wet season (typically February to May) and a
substantially polluted dry season (typically August to November) with
corresponding transition periods in between (Martin et al., 2010b; Andreae et
al., 2015). This oscillating atmospheric state is determined by the position
of the intertropical convergence zone (ITCZ) and the corresponding trade wind
circulation as well as the strong seasonality in biomass burning in Africa
and South America (Martin et al., 2010a, b; Fuchs and Cermak, 2015;
Abdelkader et al., 2017). Figure 1 serves as a general illustration of the
large-scale atmospheric circulation, as well as the predominant aerosol and
trace gas emissions patterns in the Atlantic region that govern the overall
atmospheric seasonality in the Amazon Basin. In particular, the contrasting
atmospheric states of the wet vs. dry season are emphasized.
In Fig. 1a and b, the cloud top temperature maps visualize areas with deep
convective clouds (i.e., cold cloud tops), which correspond – particularly
over the ocean – to the position of the ITCZ. During the wet season, the
ITCZ is located (slightly) south of the ATTO site, which entails a
northeasterly (NE) trade wind advection as visualized by backward
trajectory (BT) ensembles. Here, the
trades typically bring air masses from the Atlantic Ocean, which pass over
extended and almost untouched forest areas while traveling into the Amazon
Basin. During the dry season, the ITCZ is located north of the ATTO site and
the trade winds arrive from southeasterly (SE) directions. The SE trades
travel over urban and extended agricultural areas in South America and, thus,
are prone to bring substantial amounts of anthropogenic pollution into the
central Amazon. A detailed description of the corresponding land cover types
in the ATTO site's footprint can be found elsewhere (Pöhlker et al.,
2018). Note that the ITCZ-related changes between Northern and Southern
Hemispheric influences are most pronounced for the central Amazon region,
which experiences the ITCZ overpasses, whereas in the northern and southern
Amazon regions, the hemispheric air mass changes are less pronounced.
Figure 1c and d show a superposition of
precipitation and the BT ensembles, visualizing that the most intense
precipitation rates are observed co-located with the deep convection in the
ITCZ belt. The combination of precipitation maps and BT ensembles further
shows qualitatively the probability of rain-related scavenging of long-range
transported aerosols in the advected air masses along their transport track.
For the wet season, the NE trajectories meet the ITCZ rain belt at about
3∘ N. This means that for the transatlantic passage north of
3∘ N (i.e., in the subtropical latitudes) no major wet deposition of
the transported aerosol load is expected. Once the BTs intersect the rain
belt south of 3∘ N, aerosol scavenging becomes a substantial aerosol
loss mechanism, which acts as a barrier for southwards transport (Abdelkader
et al., 2017) and “efficiently scrubs out aerosols” from the advected air
masses (Andreae et al., 2012). The dry season scenario is different: here the
ITCZ rain belt is located mostly north of 2∘ N. This implies that
the SE trajectories mostly bypass the most intense rain fields. Accordingly,
the probability of aerosol scavenging during the dry season is much lower and
there is a higher likelihood that (pyrogenic and other) LRT aerosols from
Africa are transported (far) into the central Amazon Basin.
Figure 1e and f combine aerosol optical depth (AOD) maps with the BT data to
visualize the major African and South American aerosol sources that influence
the atmospheric state in the central Amazon. The main fraction of the
anthropogenic aerosol that is observed at the ATTO site is related to biomass
burning emissions from deforestation, savanna, grassland, and agricultural
waste fires in Africa and South America. These, mostly man-made, burning
activities on both continents follow a pronounced seasonality: in northern
Africa, a winter fire maximum (i.e., December–March) is observed due to
intense savanna and grassland fires in and south of the Sahel belt
(5–15∘ N), while in southern Africa a pronounced dry-season maximum
(i.e., June–October) occurs in the Miombo woodlands (5–20∘ S)
(Barbosa et al., 1999; Edwards et al., 2006). Consequently, both sources
result in extended pollution plumes mixed with dust that travel westwards
over the Atlantic Ocean, as visible in Fig. 1e and f. In South America,
strong biomass burning activity due to agriculture and deforestation fires,
occurs from August to November, mostly along the southern margin of the Amazon
forest (the so-called “arc of deforestation”) and in the extended
agricultural and Cerrado savanna areas further south (Edwards et al., 2006;
Pöhlker et al., 2018).
In addition to the biomass burning smoke, various urban and industrial
emissions in Africa (e.g., from the cities and oil rigs in the Gulf of
Guinea) and South America (e.g., from the densely populated southeastern
Brazilian coastline) may also contribute to the anthropogenic aerosol burden
(see Pöhlker et al., 2018). Figure 1g and h, show the satellite-retrieved
total carbon monoxide (CO) column, which highlights the locations of major
biomass burning activities, such as strong fire activity in the Sahel region
during the Amazonian wet season as well as fires in the tropical latitudes in
both, Africa and South America, during the Amazonian dry season.
Beside these man-made emissions, transcontinental advection of Saharan (we
use Sahara as shorthand for the dust source regions in Africa north of the
Equator) dust across the Atlantic Ocean represents a major source of LRT
aerosol that is relevant for the central Amazon (Prospero et al., 1981, 2014;
Swap et al., 1992; Formenti et al., 2001; Schepanski et al., 2009; Abouchami
et al., 2013; Di Biagio et al., 2017). The boreal summer dust plumes (August
and later) are clearly visible in Fig. 1f as they pass over the Atlantic
Ocean in a latitude belt between 12 and 20∘ N. The winter dust
plumes (February–May) in Fig. 1e pass over the ocean in a latitude belt
between 6 and 14∘ N. During the wet season conditions, the backward
trajectory bundle transects the
dust-related AOD plume over the ocean, whereas no significant overlap is
observed for the dry season months. This underlines that the Saharan desert
represents a relevant aerosol source for the Amazon Basin almost exclusively
during the wet season. It has to be noted that the wet season dust plumes are
typically mixed with smoke from African savanna fires (Koren et al., 2006;
Liu et al., 2008; Ben-Ami et al., 2010). Accordingly, the pronounced AOD
plume in Fig. 1e likely represents a mixed signal from dust and smoke as
discussed in more detail later in this work.
In the Amazonian atmosphere, a characteristic tri-modal aerosol size
distribution prevails, which – in terms of number concentrations – consists
of a rather pronounced and persistent Aitken mode, a mostly dominant
accumulation mode, and a comparatively weak coarse mode (Andreae et al.,
2015). The multimodal shape of the size distribution is the result of the
interplay of different aerosol sources as well as complex aerosol
transformation processes (for further information see Sect. S2.1 in the
Supplement) (Zhou et al., 2002; Martin et al., 2010b). The coarse
mode –
which is the focus of the present study – originates from different aerosol
sources, such as direct emissions of primary biological aerosol particles
(PBAP), marine aerosols, long-range transport (LRT) of Saharan dust plumes,
and a coarse mode fraction of biomass burning aerosols (Martin et al., 2010b;
Huffman et al., 2012). Coarse mode aerosols play several important roles in
the rain forest ecosystem. They can act as ice nuclei (i.e.,
bioaerosol particles that are ice-active at
high temperatures) and giant cloud condensation nuclei in aerosol–cloud
interaction (Prenni et al., 2009; Pöschl et al., 2010). The advected
Saharan dust particles, which mostly occur in the coarse mode, are regarded
as important fertilizers for the comparably poor Amazonian soils (e.g., by
bringing iron and phosphorous) (Swap et al., 1992; Rizzolo et al., 2017).
Furthermore, the highly abundant and diverse PBAP population ensures the
spread of various organisms in the ecosystem (Womack et al., 2015;
Fröhlich-Nowoisky et al., 2016). Further details on these major sources
of Amazonian coarse mode particles and their relevance can be found in
Sect. S2.2.
The majority of aerosol studies from the Amazon Basin has focused on the life
cycle and processing of the submicron size range of the aerosol population
and its relevance for the Earth's climate system (Martin et al., 2010b;
Artaxo et al., 2013a; Andreae et al., 2015, 2018). Its seasonal variability,
superimposed on a background of biogenic, mostly secondary organic aerosol
(Pöschl et al., 2010), is largely governed by the extent of biomass
burning activities in Africa and South America as well as the transport
patterns of the emitted aerosols. Accordingly, the anthropogenic impact on
the fine mode aerosol, including its links to biogeochemical and hydrological
cycling, is particularly pronounced. Conversely, the atmospheric life
cycle of the aerosol coarse mode is not primarily driven by a
pollution-related seasonality, but rather defined by the emission and
transport of natural aerosols (i.e., desert dust, sea spray, and primary
bioaerosols), which are released and dispersed on different spatiotemporal
scales. Whether and to what extent human activities, such as deforestation
fires and land use change, have already altered the coarse mode cycling in
the Amazon compared to the preindustrial state, is largely unknown.
Furthermore, to what extent coarse mode particles (e.g., as IN or giant CCN)
have a direct influence on clouds, precipitation, and thus the hydrological
cycle in the Amazon also bears unanswered questions. So far, only a few
studies have provided first insights into the coarse mode properties and
variability in the central Amazon (Huffman et al., 2012; Artaxo et al.,
2013b; Womack et al., 2015; Whitehead et al., 2016).
This study aims to reduce this knowledge gap by presenting a systematic
overview of the coarse mode variability from a three-year measurement period
at a remote Amazonian site. However, a comprehensive analysis of the complex
coarse mode cycling in the Amazon is clearly beyond the scope of this
manuscript. Accordingly, we focused on the following two goals: (i) first, we
aim to present a general overview of the characteristic seasonal variability
in the coarse mode concentration and size distribution to highlight annually
recurring patterns. This general characterization is supposed to serve as
baseline for follow-up studies with in-depth analyses of aspects that are
only briefly addressed here. (ii) Second, we selected the frequent transport
of African dust into the Amazon Basin, which can be observed very clearly in
the coarse mode variability. We highlight the atmospheric conditions that
explain the episodic transport of African dust into the central Amazon in
relation to the characteristic signals that are observed in the coarse mode
aerosol population as well as related parameters. Although the primary focus
of this work is the aerosol coarse mode, selected aspects of the
accumulation mode variability have also been included in the analysis and
discussion, where they help to clarify the overall picture of the relevant
atmospheric conditions and processes.
Materials and methods
Aerosol observations at the Amazon Tall Tower Observatory (ATTO)
site
The aerosol data discussed in this study have been collected at the ATTO site
(2∘08.602′ S, 59∘00.033′ W, 130 m above sea level),
which is located ∼ 150 km NE of Manaus, Brazil. The ATTO site has been
established as a long-term research station for aerosol, trace gas,
meteorological, and ecological studies in the central Amazon forest. A
comprehensive set of information on the ATTO site can be found in an overview
paper by Andreae et al. (2015). In 2014 and 2015, the ATTO site was part of
the international large-scale field campaign GoAmazon2014/5 that was
conducted in and around the city of Manaus from 1 January 2014 until
31 December 2015 (Martin et al., 2016, 2017). During GoAmazon2014/5, the ATTO
site served as the clean background site (T0a). Two intensive observation
periods (IOPs) took place: IOP1 from 1 February to 31 March 2014, and IOP2
from 15 August to 15 October 2014. Furthermore, the measurement period of
this study overlapped with the German–Brazilian ACRIDICON-CHUVA measurement
campaign in September 2014 (Machado et al., 2014; Wendisch et al., 2016),
where detailed ground-based and aircraft measurements were performed over a
large area of the Amazon Basin.
In this study, we mostly focus on measurement data obtained at the ATTO site.
However, some selected results (i.e., analysis of filter samples, see
Sect. 2.4) from the “neighbor” ZF2 site, which is located 60 km north of
Manaus and about 100 km southwest of the ATTO site, were taken into account
(Martin et al., 2010a; Artaxo et al., 2013a). A comparison of results from
the ATTO and ZF2 sites is justified by the fact that on average similar
atmospheric conditions and air mass advection patterns prevail at both
locations, as shown in Pöhlker et al. (2018). Also the aerosol (optical)
properties, analyzed in Saturno et al. (2017a), are comparable. However, some
degree of uncertainty is associated with this comparison and has to be taken
into account carefully.
Long-term measurements with optical particle sizing and data
analysis
This study is based on measurements with an optical particle sizer (OPS,
model 3330, TSI Inc. Shoreview, MN, USA), which has been operated
continuously at the ATTO site since 30 January 2014. It covers 38 months of
OPS data from 30 January 2014 until 30 April 2017. The instrument performs
single particle counting and sizing (in the range of 0.3 to 10 µm,
divided into 16 size bins) based on aerosol light scattering. All aerosol
particle sizes throughout this study represent particle diameters. The OPS
size range covers the aerosol coarse mode as well as the “tail” of the
accumulation mode, which peaks at ∼ 0.15 µm (Huffman et al.,
2012; Andreae et al., 2015). The measured aerosol number size distributions
(dN/dlogDo) were converted into surface size
(dS/dlogDo) and volume size distributions
(dV/dlogDo), assuming spherical particles with a
shape factor of 1 and a density of 1 g cm-3. The sampling intervals
and thus the time resolution of the measurements were set to 5 min.
The OPS is being operated inside a measurement container at the base of a
triangular mast, getting ambient air from a 25 mm stainless steel inlet line
with a total suspended matter (TSP) inlet head at 60 m a.g.l. and
∼ 30 m above canopy height (for further details see Andreae et al.,
2015; Pöhlker et al., 2016). The sample air is dried by silica diffusion
dryers to a relative humidity (RH) of about 40 %. From 30 January 2014
until 2 February 2015 the drying was based on frequent manual exchanges of
the silica gel cartridges. Since 2 February 2015, drying has been based on an
automated drying system as described in Tuch et al. (2009). The OPS data were
recorded by the software package “aerosol instrument manager” (AIM, version
9.0, TSI Inc.). Further analysis and processing were done with the software
packages IGOR Pro (version 6.3.7.2, Wavemetrics, Inc.; Portland, OR, USA) and
R (version 3.2.0, R Development Core Team, 2010). Periods with biased data
(i.e., due to changes of silica gel in the dryers, leaks, local
contamination) have been flagged and were not included in the present
analyses. The aerosol data reported here were converted to standard
temperature (0 ∘C) and pressure (1013 hPa) (STP). The time
throughout this study is shown as coordinated universal time (UTC). Local
time (LT or UTC - 4) has only been used for the analysis of diurnal
cycles and is marked accordingly.
Optical aerosol sizing techniques have been widely used in aerosol research.
However, the following aspects have to be kept in mind: (i) optical aerosol
sizing – based on the correlation of particle size and the intensity of
light scattering pulses – is one of three widely used approaches to retrieve
equivalent diameters of coarse mode particles. The other experimental
strategies rely on geometric or aerodynamic particle sizing (Gwaze et al.,
2007; Huffman et al., 2012). (ii) The resulting equivalent optical
(Do), geometric (Dp), and aerodynamic
(Da) diameters and corresponding aerosol size distributions
typically deviate substantially due to systematic experimental biases.
(iii) In a direct comparison of Do, Dp, and
Da retrievals, Reid et al. (2003a) stated that optical sizing has
the largest biases of all three techniques. Specifically, it tends to
oversize particles (up to factor 2) and results in a broadening of the coarse
mode. These aspects have to be critically considered throughout the
subsequent discussion of the OPS-derived results in this study.
Inlet aspiration and transmission efficiency with particle loss
corrections
Online aerosol analysis and sampling in the field typically rely on dedicated
inlet systems that transport the ambient air to the instruments and/or
samplers. However, sampling through an inlet is always non-ideal and, thus,
creates sampling artifacts as well as biases the measurement results (von der
Weiden et al., 2009). Representative aerosol analysis requires high
aspiration rates (i.e., isoaxial and isokinetic conditions) and high tube
transmission efficiencies (i.e., minimized losses) (von der Weiden et al.,
2009; Byeon et al., 2015). For the tube transmission, the most relevant
particle loss mechanisms (i.e., diffusion, sedimentation, and inertial
deposition) are strongly size dependent, and coarse mode aerosol particles,
which are the focus of the present study, are particularly prone to the
deposition- and inertia-related artifacts. Since the overall sampling
efficiency is critical for the representativeness and interpretation of the
coarse mode results reported here, we conducted a detailed analysis of
relevant particle losses in the inlet system and applied a corresponding
particle loss correction to the OPS data. Details can be found in Sect. S1.1.
Aerosol filter sampling for gravimetric and x-ray fluorescence
analyses
Aerosol filters for gravimetric, x-ray fluorescence, and further analysis are
collected at the ATTO site on a continuous basis. Prior to February 2016, the
samples were collected with a two-stage impactor. After February 2016, the
samples were collected with an automated
Partisol™ filter sampler (model 2025i,
Thermo Fisher Scientific, Waltham, MA, USA). The samplers were placed on a
platform on the walk-up tower at a height ∼ 50 m above ground level
(Andreae et al., 2015). The sampling was conducted size-segregated on a
“fine” mode filter (with D < 2.5 µm) and a “coarse” mode
filter (with 2.5 < D < 10 µm). The samples were mostly
collected on a 5–7 day basis with a low volume sampler (16.7 L min-1)
with 47 mm diameter and 0.4 µm pore size polycarbonate filters. A
similar continuous filter sampling has been conducted at the ZF2 site as
well.
The gravimetric analysis was conducted according to a protocol of the US
Environmental Protection Agency. Mass concentrations were obtained
gravimetrically using an electronic microbalance with a readability of
1 µg (Mettler Toledo, model MX5) in a controlled-atmosphere room
under defined conditions (i.e., RH 35 % and 20 ∘C). Filters were
equilibrated for 24 h prior weighing. Electrostatic charges were controlled
with radioactive 210Po sources. The experimental error in the elemental
mass concentrations is specified as ±5 % (see details in Artaxo et
al., 2002, 2013b).
List of African LRT episodes observed at the ATTO site from
February 2014 until April 2017. The mean and maximum M1–10
values characterize the relative intensity of the dust pulses. For LRT
episodes that contain larger data gaps, the M1–10 values were
put in parentheses. Note that the mean and maximum M1–10 values
shown here are based on a density of 1 g cm-3, see further information
in Sect. 2.3. Since typical densities of dust and sea salt components in the
LRT plumes are higher (see Table S1), the shown M1–10 values
represent lower limit values (compare Table 2).
LRT
Date [UTC]
M1–10 [µg m-3]
Episodes
Start
End
Mean
Max
2014_1
2 Feb 2014
5 Feb 2014
7.60
22.32
2014_2
9 Feb 2014
21 Feb 2014
(12.46)
(77.04)
2014_3
2 Mar 2014
12 Mar 2014
(14.90)
(41.67)
2014_4
20 Mar 2014
24 Mar 2014
10.11
26.44
2014_5
28 Mar 2014
1 Apr 2014
7.07
15.30
2014_6
3 Apr 2014
5 Apr 2014
7.37
16.59
2014_7
8 Apr 2014
14 Apr 2014
(10.84)
(24.40)
2015_1
2 Feb 2015
18 Feb 2015
(8.47)
(23.89)
2015_2
27 Feb 2015
9 Mar 2015
(9.99)
(38.81)
2015_3
11 Mar 2015
14 Mar 2015
8.74
25.55
2015_4
18 Mar 2015
25 Mar 2015
9.04
39.75
2015_5
2 Apr 2015
10 Apr 2015
(19.17)
(59.65)
2016_1
6 Dec 2015
12 Dec 2015
17.80
71.34
2016_2
22 Dec 2015
25 Dec 2015
9.48
16.92
2016_3
28 Dec 2015
2 Jan 2016
11.64
30.75
2016_4
4 Jan 2016
11 Jan 2016
11.97
40.85
2016_5
20 Jan 2016
31 Jan 2016
(8.14)
(20.83)
2016_6
5 Feb 2016
22 Feb 2016
(12.88)
(62.44)
2016_7
2 Mar 2016
4 Mar 2016
12.59
37.49
2017_1
29 Dec 2016
11 Jan 2017
10.76
25.75
2017_2
8 Feb 2017
15 Feb 2017
18.03
46.71
2017_3
25 Feb 2017
26 Feb 2017
(6.94)
(14.34)
2017_4
11 Mar 2017
18 Mar 2017
6.23
28.35
2017_5
1 Apr 2017
6 Apr 2017
25.91
106.17
2017_6
14 Apr 2017
15 Apr 2017
7.20
31.51
2017_7
20 Apr 2017
23 Apr 2017
8.41
27.60
Energy dispersive x-ray fluorescence (EDXRF) analysis was used to quantify
elements with atomic numbers ≥ 11 (Na and heavier) on the filters
(Arana et al., 2014). The elements sodium (Na), magnesium (Mg), aluminum
(Al), silicon (Si), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K),
calcium (Ca), titanium (Ti), manganese (Mn), and iron (Fe) have been analyzed
in detail in the context of this study. The detection limits for the
individual elements are shown in Table S3 in the Supplement, based on the
work by Arana et al. (2014). The coloring of the elements in the
corresponding figures was done according to the Corey–Pauling–Koltun (CPK)
color schema (http://jmol.sourceforge.net/jscolors/, last access:
12 October 2017). For the chemical analysis of LRT aerosols as well as
aerosols in the absence of strong LRT influence (called non-LRT conditions
here), the EDXRF results from five LRT filter and four non-LRT filter samples
were taken into account. The selection of filter samples for this analysis is
based on the online data analysis: LRT samples span LRT episodes according to
Table 1 and non-LRT samples are free of detectable LRT influence. Samples
spanning both LRT and non-LRT conditions were omitted in this analysis
accordingly. The LRT filters were collected on (i) 6 February 16:18 (UTC) to
9 February 16:52 2015, (ii) 27 February 15:10 to 2 March 17:16 2015,
(iii) 11 March 17:23 to 13 March 15:54 2015, (iv) 20 March 16:41 to 23 March
16:34 2015, and (v) 2 April 16:41 to 8 April 16:48 2015. The non-LRT filters
were collected on (i) 23 February 17:16 to 27 February 14:57 2015,
(ii) 27 March 15:51 to 2 April 16:28 2015, (iii) 15 April 17:21 to
20 April 16:10 2015, and (iv) 20 April 16:22 to 24 April 15:43 2015. We
report the obtained elemental results for the D < 2.5 µm and
2.5 < D < 10 µm size fractions as well as total
concentrations and mass fractions,
corresponding to the sum of both size fractions. The shown elemental mass
concentrations for the LRT case represent average values based on the five
LRT filters and the non-LRT results represent averages of the four non-LRT
filters. Elemental mass fractions were calculated relative to the
gravimetrically determined total mass loading of the filters. For the
estimation of elemental deposition fluxes, the difference in mass
concentration MLRT(X) - Mnon-LRT(X) for a
certain element X as well as the difference in total mass concentration
MLRT,total - Mnon-LRT, total were taken into
account.
Comparison of OPS-retrieved aerosol masses and gravimetric
analysis
The calculated aerosol mass concentrations, which are based on the OPS
measurements and the PLC (see Sect. 2.3), were validated by means of a
comparison with a gravimetric filter analysis, based on ATTO aerosol filters, as outlined in Sect. 2.4. For
the comparison, the following two periods with gravimetric results were
available: (i) a period from 6 September to 29 November 2014, comprising 10
filters and (ii) a period from 30 September to 8 November 2015, comprising 14
filters. No corresponding gravimetric results were available for the wet
season and LRT episodes. The results of the comparison are summarized in
Fig. S2 in the Supplement, which shows the coarse mode aerosol mass time
series (Fig. S2a and b) as well as a scatter plot with the combined results
(Fig. S2c). Figure S2 confirms that the aerosol masses from the direct
gravimetric approach and the indirect OPS-based retrieval agree comparatively
well. We regard the gravimetric analysis as reference measurement, which
confirms that the PLC of the OPS data and the chosen density (i.e.,
ρ1.0) are appropriate to yield reliable OPS-based aerosol mass
concentrations at the ATTO site in the absence of major dust influence. This
implies that during substantial influence of dust and/or sea salt, which are
characterized by higher aerosol particle densities (∼ 2 g cm-3,
see Table S1), no one-to-one agreement between OPS-based mass retrieval and
gravimetry can be expected. In fact, the OPS-derived aerosol mass would
underestimate the actual mass. The influence of different densities on the
aerosol mass concentration is addressed later in this study in more detail
(refer to Table 2).
Supplementary aerosol measurements and instrument comparison
A broad set of aerosol and trace gas instrumentation is being operated at the
ATTO site on a continuous basis (Andreae et al., 2015). In addition to the
OPS data analysis, which is the focus of the present study, we used
supplementary data from the following online instruments: a condensation
particle counter (CPC, model 5412, Grimm Aerosol Technik, Ainring, Germany),
a scanning mobility particle sizer (SMPS, model 3082, TSI Inc., Shoreview,
MN, USA), an ultra-high sensitivity aerosol spectrometer (UHSAS, DMT,
Longmont, CO, USA), a wide-band integrated bioaerosol sensor (WIBS, model 4A,
DMT), a three-wavelength integrating nephelometer (Ecotech Aurora 3000,
λ = 450, 525, and 635 nm), a multi-angle aerosol absorption
photometer (MAAP, model 5012, Thermo Electron Group, λ = 670 nm), carbon dioxide (CO2), methane (CH4), and carbon
monoxide (CO) monitors based on cavity ring-down spectroscopy (G1301, G1302
analyzers, Picarro Inc, USA) and a ceilometer (CHM15kx, Jenoptik, Germany).
The equivalent black carbon (BCe) mass concentrations,
MBCe, were obtained from the MAAP aerosol absorptivity
measurements based on ATTO-specific mass absorption coefficients (MAC) as
discussed in Saturno et al. (2017a). The ceilometer has been operated at the
ATTO site since December 2014. It has been installed with an inclination
angle of 15∘ to minimize direct sunlight. Frequent comparisons of
the sizing instruments have been conducted at the ATTO site to ensure
comparability of the individual data sets. Figure S3 presents the results
of such a comparison for the instruments OPS (0.3–10 µm), SMPS
(0.01–0.43 µm), UHSAS (0.06–1 µm), and WIBS
(1.5–10 µm). The good agreement of the size distributions shows
that the results of all instruments are consistent. The ceilometer data was
processed according to Heese et al. (2010), specifically dedicated to detect
aerosol layers in the free troposphere up to ∼ 4 km height during
daytime hours.
Backward trajectory analysis
Our investigation of the atmospheric transport in this study is based on a
systematic backward trajectory (BT) analysis, which has been adopted from a
recent study where details can be found (Pöhlker et al., 2018). Briefly,
Fig. S4 shows 15 clusters of 3-day BT ensembles, which describe the
spatiotemporal variability of air mass movement towards the ATTO site over
the NE Amazon Basin. The choice of 15 clusters is explained in Pöhlker et
al. (2018). The choice of 3-day BTs is based on the following rationales:
(i) The mesoscale circulation that transports the African LRT aerosol plumes
from the Atlantic coast into the Amazon Basin are of primary interest for
this study. In this regard, the 3-day BTs sufficiently cover the relevant NE
fetch across the basin. (ii) Moreover, the region of interest
ROIoff (see details in Sect. 2.8) is relevant for rain-related
aerosol scavenging (i.e., of LRT aerosol, see Fig. 1c and d), which is of
particular relevance for the analyzed phenomena. Figure S4 illustrates that
the air masses at the ATTO site arrive almost exclusively in a rather narrow
easterly wind sector (between 45 and 120∘). Within this sector, four
BT subgroups can be identified: (i) a northeasterly (NE) track including the
clusters NE1, NE2, and NE3; (ii) an east–northeasterly (ENE) track including
the clusters ENE1, ENE2, ENE3, and ENE4; (iii) an easterly (E) track
including the clusters E1, E2, E3, and E4; and (iv) a group of “inland”
trajectories in east–southeasterly (ESE) directions including clusters ESE1,
ESE2, and ESE3 as well as one cluster towards the southwest (SW1).
Furthermore, the cumulative precipitation along the BT tracks, PBT,
has been calculated based on the HYSPLIT model output. Note that the HYSPLIT
precipitation output is provided “at the grid cell where the trajectory is
located and does not depend on the cloud value at the height of the
trajectory” (comment by G. Rolph at
https://hysplitbbs.arl.noaa.gov/viewtopic.php?t=577, last access:
15 March 2018).
Satellite data analysis
The satellite data products used in this study were obtained from the
Giovanni web-based application by the Goddard Earth Sciences Data and
Innovation web interface (http://giovanni.gsfc.nasa.gov/, last access:
4 July 2017) (Acker and Leptoukh, 2007). The following satellite products
were used: (i) aerosol optical depth (AOD) at a wavelength of 550 nm from
the moderate resolution imaging spectroradiometers (MODIS) on the satellites
Terra and Aqua (combined dark target deep blue AOD products MOD08_D3_V6
and MYD08_D3_V6), (ii) cloud top temperature data from the atmospheric
infrared sounder (AIRS) instruments on board of Terra and Aqua
(AIRX3STD_v006 product), (iii) precipitation data from the tropical
rainfall measuring mission (TRMM) mission (TRMM_3B42_Daily_v7
product), and (iv) CO total column data from the AIRS instruments on board of
Terra and Aqua (AIRS3STD_v006 product). These data sets were used as
time-average maps as well as time series for specified regions of interest
(ROI). The time series of MODIS and TRMM satellite data products were
obtained as area averages within two ROIs: (i) a ROI in front of the NE coast
of the basin (“offshore”), called ROIoff (49 to 37∘ W;
0 to 12∘ N) and (ii) a ROI covering the ATTO region, called
ROIATTO (59.5 to 54∘ W; 3.5∘ S to
2.4∘ N); both ROIs are displayed in Fig. S4. The data files were
exported as netcdf (Network Common Data Form, version 3.x) or ascii files.
Further data processing was conducted in IGOR Pro. Cloud Lidar Aerosol
Infrared Pathfinder Satellite (CALIPSO) data products – i.e., lidar profiles
from the cloud-aerosol lidar with orthogonal polarization (CALIOP) – were
obtained from the website: https://www-calipso.larc.nasa.gov/ (last
access: 15 June 2017). Daily wind data and precipitation were obtained from
National Center for Environmental Prediction NCEP
(http://www.esrl.noaa.gov/psd/data, last access: 20 September 2016) and
using the software MeteoInfo (see details in Wang, 2014).
GEOS-Chem modeling
The modeling results used here are based on GEOS-Chem version 9-02
(http://www.geos-chem.org/, last access: 17 May 2017). GEOS-Chem is a
chemical transport model with a global 3-D model of atmospheric composition
driven by assimilated meteorological data GEOS-5 FP from the NASA Global
Modeling and Assimilation Office (GMAO). Aerosol types simulated in GEOS-Chem
include carbonaceous aerosols (fine mode), sulfate–nitrate–ammonium
aerosols (fine mode), fine and coarse mode sea salt, and mineral dust in four
size classes. For details, we refer the reader to a previous study by Wang et
al. (2016), which showed that GEOS-Chem successfully captured the observed
variation in aerosol properties in the Amazon Basin during
January–April 2014.
Amazonian seasonality, nomenclature, and definition of LRT
episodes
This study utilizes the definition of the Amazonian seasons according to M.
Pöhlker et al. (2016) as follows: (i) the “wet season” spans February
to May and shows the cleanest atmospheric state, (ii) the “transition period
from wet to dry season” spans June and July, (iii) the “dry season” months
August to November show the highest pollution levels, and (iv) the “transition
period from dry to wet season” spans December and January. For the present
analysis, all results referring to “transition period” include both, the
transition period from wet to dry season and the transition period from dry
to wet season. Results referring to the “LRT episodes” of a certain year or
for the entire measurement period represent averaged results from the
individual LRT episodes as listed in Table 1. Note that LRT episodes in this
study refer exclusively to LRT during the Amazonian wet season, when African
dust and smoke has been transported to the Amazon. During the dry season, LRT
aerosols (mostly smoke without dust) from southern Africa arrive in the
Amazon Basin via similar transport mechanisms. These dry season LRT events,
which typically do not transport substantial dust loads and, thus, are less
relevant for the coarse mode, have not been addressed in the context of this
study. Details regarding dry season LRT aerosols can be found in Saturno et
al. (2017a, b). Results referring to the “wet season without LRT” represent
average data from the wet season time frame with the corresponding LRT
episodes in Table 1 being excluded.
Overview of the long-term meteorological and aerosol time series
emphasizing coarse mode data, in the context of atmospheric seasonality at
the ATTO site, with time series representing daily averages.
(a) Frequency of occurrence, fBT,clusters, of 15 back
trajectory clusters as displayed in Fig. S4 (with identical color coding).
(b) Image plot of aerosol surface size distribution for the size
range from 80 nm to 10 µm. (c, left axis) Time series of
the aerosol scattering coefficients, σsp, at three different
wavelengths. (c, right axis) Time series of equivalent black carbon
(BCe) with mass absorption cross-sections (MAC) at 637 nm that
represent conditions in the ATTO region (wet season: 11.4 m2 g-1;
dry season: 12.3 m2 g-1). (d, left
axis) Satellite-retrieved aerosol optical depth at 550 nm, area-averaged
over offshore region of interest (ROIoff, see Fig. S4). The
AODROI,off time series represents the average of the MODIS data
sets from the satellites Aqua and Terra (d, right axis)
HYSPLIT-retrieved accumulated precipitation PBT along the
trajectory tracks. (e) Aerosol mass concentrations in the size
ranges 0.3–10 µm (M0.3–10), 1–10 µm
(M1–10), and 0.3–1 µm (M0.3–1).
Colored shading in the top of the figure visualizes the Amazonian seasons
(see Sect. 2.10) as well as the intensive operation periods (IOPs) 1 and 2 of
the GoAmazon2014/5 project. Gray vertical bands mark episodes when Saharan
LRT aerosol was measured at the ATTO site (see Table 1).
![]()
As LRT episodes, we defined periods with continuous dust influence at the
ATTO site, when the following three criteria were fulfilled: (i) increased
AOD values were detected by the spaceborne MODIS instruments (see examples in
Figs. 11 and S11) for clear detection of African dust outbreaks,
transatlantic passage, and arrival at the South American coast, (ii) the
maximum of daily averaged coarse mode mass concentrations, M1–10
exceeded 9 µg m-3 (average wet season
concentration + 2 standard deviations), as a sensitive marker for the
abundance of supermicron particles, and (iii) the M1–10 time
series showed a peak-like increase that lasted longer than one day. Episodes
with chemical information on aerosol composition (i.e., based on weekly
filter samples, see Sect. 2.4) confirm that the aforementioned criteria are
sufficient to identify major and medium dust events. The observed LRT
episodes typically span periods from 2 to 20 days. However, it has to be
noted that the definition of the (precise) beginning and end of each LRT
episodes may be considered arbitrary to some extent because of the
comparatively high variability of the M1–10 background.
Results and Discussion
Time series of the aerosol coarse mode and<?xmltex \hack{\break}?> related parameters
The overview Fig. 2 shows coarse mode aerosol data from almost two and a half
years (February 2014 until June 2016). It combines this data with selected meteorological and aerosol
time series and, thus, puts the coarse mode trends in the context of the
overall atmospheric seasonality at the ATTO site. Figure 2a displays the
daily frequency of occurrence of the individual BT clusters,
fBT,cluster (see Fig. S4 for BT cluster overview). This shows the
clear prevalence of northeasterly (NE) and east–northeasterly (ENE)
trajectories during the wet season (i.e., February–April), followed by a
rather sudden shift to easterly (E) and east–southeasterly (ESE) trajectories
towards the end of the wet season (i.e., May). During the subsequent
transition period (i.e., June–July) and most of the dry season (i.e.,
August–October), E and ESE trajectories prevail. Eventually, at the end of
the dry season (i.e., November) and in the subsequent transition period
(i.e., December–January), the dominant wind direction gradually swings back
from SE to NE directions.
In general, the seasonality in the atmospheric composition in the central
Amazon is largely driven by the seasonal cycle of vegetation fire activity in
combination with the changing air mass transport patterns. This biomass
burning seasonality is detectable in most aerosol parameters (Andreae et al.,
2015; Pöhlker et al., 2016). As examples in Fig. 2b and c, the
accumulation mode abundance, MBCe, and the aerosol scattering
coefficient, σsp, time series showed pronounced maxima, with
highest concentrations during the core months of the dry season (i.e.,
August–October). In this overall picture, the coarse mode seasonality shows
different trends. This can be seen in the plot of the surface size
distribution (i.e., from 1 to 10 µm, Fig. 2b) as well
as in Fig. 2e as time series of total aerosol mass concentration covering the
size ranges 0.3–10 µm (M0.3–10), 1–10 µm
(M1–10), and 0.3–1 µm (M0.3–1).
Particularly, M1–10 represents a sensitive indicator for the
overall abundance of coarse particles (i.e., > 1 µm). The coarse
mode aerosol mass concentration, M1–10, was generally low and
ranged below 10 µg m-3 for most of the measurement period.
The trend was only interrupted by annually reoccurring and well defined
peaks, which mostly occurred in the period December to April as detectable in
Fig. 2b and e.
The observed peaks in M1–10, which Fig. 2 highlights with gray
vertical shading, represent the frequent intrusion of African LRT dust and
combustion aerosol plumes (called here LRT episodes). The identification of
major LRT episodes in the ATTO data was rather straightforward as they are
associated with pronounced increases in the aerosol parameters,
M1–10 and MBCe. However, the detection of minor
and/or “diluted” LRT episodes turned out to be difficult in certain cases,
due to the variable coarse mode background, which is mostly driven by PBAP
cycling. The criteria that were used for the definition of LRT episodes are
outlined in Sect. 2.10. Table 1 summarizes all detected major and medium LRT episodes
from February 2014 to April 2017. Note that the M1–10 peaks
clearly coincide with corresponding signals in MBCe and
σsp, underlining that the LRT aerosols typically represent
mixtures of Saharan dust (mostly in the coarse mode), biomass burning smoke
(mostly in the accumulation mode), and sea spray (in the coarse and
accumulation modes) (Quinn et al., 1996; O'Dowd et al., 2008; Wang et al.,
2016; Aller et al., 2017; Huang and Jaeglé, 2017). Accordingly, the image
plot of the aerosol size distribution shows (pronounced) LRT pulses in both
the super- and submicron size ranges. Note in this context that also the
scattering Ångström exponent, åsca, is a
sensitive indicator for the presence of coarse mode particles. Qualitatively,
this effect can be seen in Fig. 2c by means of a decreasing difference of the
three σsp time series during LRT episodes. Quantitatively,
this effect is shown by Saturno et al. (2017a) in a multi-year
åsca time series from the ATTO site with clear
åsca decreases during the presence of African dust
aerosols. Mostly, the LRT episodes occurred in a rather defined time window
from December to April, which was described previously as the “Amazonian
dust season” (Andreae, 1983; Swap et al., 1992; Formenti et al., 2001;
Rizzolo et al., 2017).
The pulse-wise arrival of African LRT aerosol plumes at the ATTO site, as
shown in Fig. 2, appears to be controlled by the following three factors:
(1) arrival and availability of LRT aerosol plumes at the South American
coast, (2) atmospheric circulation in the NE Amazon Basin and its efficiency
to transport dust from the coast towards the ATTO site, and (3) the extent of
wet deposition of the aerosol load en route. Note that (2) and (3)
are related to some extent since aerosol deposition by precipitation plays a
central role in both of them. However, we decided to outline both aspects
separately for the sake of clarity.
Relating to (1), Saharan dust outbreaks are frequent and circulation patterns
and aerosol deposition over the Atlantic Ocean define if, and to what extent,
the LRT plumes arrive at the NE margins of the basin (Gläser et al.,
2015). To analyze this arrival of dust plumes on a temporal scale with
area-averaged satellite data, we defined an (offshore) region of interest
(ROIoff) NE of the Amazon River delta, over the Atlantic Ocean, as
displayed in Fig. S4. This ROIoff intersects with the main tracks
of the wet season trajectory clusters (i.e., NE and ENE) and further covers
the arriving LRT plumes. The resulting satellite-derived and
ROIoff-averaged AOD time series (AODROI,off) is
displayed in Fig. 2d. Note that the transport time of the air masses over the
last ∼ 1500 km from the ROIoff to the ATTO site takes on
average 3 days (Pöhlker et al., 2018). In order to (qualitatively)
compare the M1–10 signals at the ATTO site with the
AODROI,off variability of arriving dust plumes at the coast, we
“synchronized” both time series via lagging the AODROI,off
data by 3 days. In other words, the direct comparison of the shifted
AODROI,off time series shows the amount of dust that
potentially arrived at the ATTO site (Fig. 2d) vs. the M1–10
time series that indicates the amount of dust that actually arrived
(Fig. 2e). This comparison (Fig. 2d vs. e) indicates that only a rather small
fraction of the dust load at the coast actually reached the ATTO site (i.e.,
the majority of AODROI,off peaks did not result in
M1–10 signals). The following examples in Fig. 2 illustrate
these trends: the pronounced dust pulses at the ATTO site around 18 February,
7 March, and 10 April 2014 were clearly related to corresponding
AODROI,off increases. However, note that the intensities of
M1–10 and AODROI,off in this relationship did not
necessarily correlate. For instance, the M1–10 pulse on
6 April 2015, which represents one of the strongest signals observed during
the entire measurement period, was associated with a comparatively weak
signal in AODROI,off. Furthermore, all April–May periods in
Fig. 2 showed pronounced and continuous AODROI,off signals,
however only sparse dust transport towards the ATTO region as shown by the
few occurring M1–10 peaks. To further underline these aspects,
the Fig. S5 focuses specifically on a direct M1–10 vs.
AODROI,off comparison. In summary, the AODROI,off
vs. M1–10 time series underline that the LRT plume transport
from the coast over the NE region of the basin towards the ATTO site can be
regarded as a “bottleneck”, as most of the LRT aerosol load appears to be
scavenged on its way here, which is subject of detailed discussion in the
following paragraphs.
Relating to (2), a second key factor appears to be the efficiency of dust
transport coming from the coast to the ATTO site. In this context, efficiency
means a direct and fast transport of air masses from regions with enhanced
arriving dust loads in front of the coast towards the ATTO site. This
relationship can be illustrated by means of the frequency of occurrence of
the BT clusters fBT (Fig. 2a). Specifically, the occurrence of
M1–10 peaks appears to be predominantly associated with high
fBT of those BT clusters that reach furthest to the NE: namely
NE2 and NE3. The majority of dust pulses in Fig. 2 followed this trend. To
mention a few characteristic examples: all M1–10 pulses
from February to April 2014 precisely
coincide with the purple-bluish peaks of high NE2 and NE3 fBT in
Fig. 2a. The efficiency aspect of the transport can be further explained by
the spatiotemporal trends in Fig. 3, which display the longitude-averaged
Hovmöller plots for satellite-derived AOD levels and precipitation rate
PTRMM. The geographic dimensions of the Hovmöller plots are
based on the ROIoff: the longitudinal range (49 to
37∘ W), which is averaged in the Hovmöller representation, is
identical in the ROIoff. Moreover, the latitudinal range of the
Hovmöller plot (10∘ S to 30∘ N) includes the
ROIoff (0 to 12∘ N), however, it reaches further north
and south. Figure 3a illustrates the annual latitudinal shifts in the LRT
plume position (Huang et al., 2010). Thus, at the beginning of the year
during February and March, the southernmost position was observed with an AOD
maximum at ∼ 6∘ N. The northernmost position is observed in
July and August with an AOD maximum at ∼ 17∘ N. In parallel, a
pronounced spatiotemporal shift can be found in PTRMM:
particularly in March the southernmost position of the PTRMM
maximum is observed at ∼ 0∘ N, whereas the northernmost
position occurs in September at ∼ 7∘ N, which corresponds with
the ITCZ shifts as previously shown in Fig. 1c and d.
Longitude-averaged Hovmöller plots
for (a) MODIS-derived aerosol optical depth (AOD) and
(b) TRMM-derived precipitation rate, PTRMM. The average
AOD data represent a time period from July 2002 to June 2016. The average
precipitation rate data represent a time period from December 1997 to
March 2016. Long time periods have been chosen (maximum of available
satellite data) to extract representative seasonal trends. The averaged
longitude range of the plots from 49 to 37∘ W corresponds with the
longitude dimension of the ROIoff in Fig. S4. The latitudinal
range of the Hovmöller plots spans from 10∘ S to 30∘ N.
The solid black lines mark the latitudinal margins of the ROIoff
(0 to 12∘ N) and the black dashed lines mark the start (December) and
end (April) of the period when most African LRT aerosol transport towards the
ATTO site has been observed.
![]()
In the light of Fig. 3, the high dust transport efficiency of the BT clusters
NE2 and NE3 can be explained as follows: (i) both clusters reached
comparatively far to the north and, thus, had the highest overlap with the
densest LRT plume regions (i.e., both clusters transect the AOD maximum at
∼ 7∘ N; compare Figs. 1e and 3a). Thus, they tended to
transport (on average) the highest LRT aerosol loads into the basin.
(ii) Both clusters are comparatively “long”, which reflects high air mass
velocities and, thus, minimizes the probability of aerosol scavenging.
(iii) Moreover, both clusters bypassed the densest rain fields in the north
and, thus, tended to avoid intense scavenging (compare Figs. 1c and 3b).
Overall, this study clearly shows that the clusters NE2 and NE3 are
particularly efficient dust transporters, as they have maximum overlap with
the high-AOD region and, at the same time, a rather small overlap with
high-PTRMM regions. In contrast, the ENE clusters have less
overlap with the dense AOD fields and higher probability to receive larger
amounts of precipitation. Accordingly, periods with high frequency of
occurrence of the ENE clusters were typically not associated with increased
M1–10 levels. The influence of the air mass transport track and
rain fields is discussed in further detail by means of a case study in
Sect. 3.5.
Relating to (3): wet deposition is the dominant aerosol loss mechanism in
tropical latitudes because of their intense precipitation (Huang et al.,
2009; Martin et al., 2010a; Abdelkader et al., 2017). According to Fig. 1c,
comparatively small scavenging rates are expected for most of the dust's
transatlantic passage (i.e., north of 3∘ N), whereas precipitation
rates (on average) increase instantaneously when the air masses meet the ITCZ
rain belt. A direct comparison of the cumulative precipitation,
PBT, for 3-day vs. 9-day BTs shows that most of the rain (on
average ∼ 75 %, see Fig. S6) occurred during the last 3 days of the
air mass journey, underlining that the region of the ITCZ belt is most
important for aerosol wet deposition. Along these lines, the daily averages
of PBT along the 3-day BT tracks represent a measure for the
extent of scavenging rates that the air masses experience in the NE basin.
The intense precipitation in the NE basin defines if and to what extent the
LRT plumes reached the ATTO region. The PBT time series shown in
Fig. 2d and its comparison with M1–10 in Fig. 2e clearly
underlines this relationship: virtually all M1–10 pulses
correspond with relative minima in the PBT time series and vice
versa. This shows, expectedly, that the dust burden arriving at ATTO was
inversely related to the cumulative amount of rain that the corresponding air
masses experienced. In other words, only dust plumes that survived the
intense rain-related scavenging had a chance to arrive in the ATTO region.
Good examples for this relationship (among many others) are the dust pulses
around 18 February 2014 and 6 April 2015. Note that the HYSPLIT precipitation
output, which is calculated per grid cell, does not depend on altitude and
cloud cover (see Sect. 2.7). Thus, this analysis does not exclude that dust
is transported at high altitudes over the precipitating clouds and mixed
downwards in the ATTO region. However, the clear inverse relationship between
the PBT and M1–10 variability underlines empirically
that PBT can be used as a simple but reliable proxy for the
extent of rain-related scavenging.
Based on the time series in Fig. 2, we calculated mean seasonal cycles of
selected aerosol, trace gas, and meteorological parameters, which were
combined into Fig. 4. Meteorologically, Fig. 4a shows the seasonal BT
patterns. The annual oscillation between wet vs. dry season BTs stands out
clearly. More specifically, the BT patterns illustrate the timing of changes
in the dominant wind direction, such as its swing from NH to SH and back upon
latitudinal passage of the ITCZ. Further details related to Fig. 4a can be
found elsewhere (Pöhlker et al., 2018). Figure 4b shows the seasonality
of two different precipitation parameters: PBT and the
TRMM-derived precipitation rate, PTRMM, in the ROIATTO (see Fig. S4). The
PBT data represents a measure for the aerosol scavenging in the
transported air masses and, thus, provides important information about (LRT)
aerosol removal en route. In contrast, the PTRMM data represents
a regional characterization of the precipitation in an area around ATTO. The
seasonality in PTRMM shows a rather broad maximum spanning most
of the wet season (i.e., February–May), whereas the seasonal trends in
PBT show a comparatively narrow and well pronounced maximum in
April and May. Figure 4c shows the seasonality of the pollution tracers
MBCe and carbon monoxide mole fraction, cCO,
reflecting the pronounced biomass burning seasonality in South America as
well as Africa as a major source of LRT pollution. The cleanest episodes in
terms of pollution aerosols (see MBCe) occurred between April and
May, which could be explained by the maximum in aerosol scavenging (see
highest PBT values in April and May) (see Pöhlker et al.,
2017). In parallel, a minimum in biomass burning occurrence is typically
found during this period (i.e., the Amazonian burning season has not started
yet and the frequency of savanna fires in West Africa declines after
February) (Andreae et al., 2015). The seasonality of the MODIS-derived
parameter, AODROI,off, in Fig. 4d, representing arriving LRT
plumes in the NE coast of the basin, shows a comparatively broad peak with a
maximum around March.
Seasonal cycle of selected meteorological, trace gas, and aerosol
parameters in the central Amazon. (a) Weekly frequency of occurrence
of backward trajectory clusters
according to Pöhlker et al. (2018). (b) Precipitation products
PBT, representing cumulative precipitation along BT tracks, and
PTRMM, representing TRMM-derived precipitation within
ROIATTO (see Fig. S4). (c) Pollution tracers
BCe mass concentration, MBCe, and carbon monoxide
mole fraction, cCO. The BCe data includes ATTO and
ZF2 site measurements, spanning from 2008 to 2017. CO data includes ATTO site
measurements from 2012 to 2017. (d) MODIS-derived AOD data within
ROIoff as defined in Fig. S4, spanning from 2000 to 2016.
(e) OPS-based coarse mode aerosol mass in two configurations: with
and without LRT peaks, according to Table 1. Data are shown as weekly
averages with error bars representing one standard deviation.
![]()
Statistical distributions (box-and-whisker plots) of
(a) total aerosol number concentration (> 4 nm, CPC derived),
(b) BCe mass concentration, MBCe,
(c) aerosol number concentration in the size range
0.3–1 µm, (d) aerosol mass concentration in the size
range 0.3–1 µm, (e) aerosol number concentration in the
size range 1–10 µm, and (f) aerosol mass concentration in
the size range 1–10 µm. Wet season, LRT episodes, transition
periods, and dry season were defined according to Sect. 2.10. Data included
here span from February 2014 to April 2017. The boxes show the median (thick
horizontal line), mean (black dot), 25 percentile (Q1, lower border of the
box), and 75 percentile (Q3, upper border). The range between Q1 and Q3 is
called interquartile range: IQR = Q3–Q1. The lower whisker shows the
lowest measured value still within 1.5 IQR of the lower quartile, and the
highest measured value still within 1.5 IQR of the upper quartile, see
Tukey (1977).
![]()
Figure 4e shows the seasonality of the coarse mode aerosol abundance
(represented by M1–10) at ATTO, which is displayed in two
variations: (i) as a data set that includes the entire time series and, thus,
reflects the M1–10 seasonality with all coarse mode-relevant
aerosol sources (“M1–10 with LRT”) and (ii) as a data set
excluding all LRT periods as defined in Table 1 and, thus, reflecting the M1–10 seasonality without (most of) the African LRT influence
(“M1–10 without LRT”). The “M1–10 with LRT”
data expectedly shows its highest levels in the dust season (December–April)
with weekly average concentrations frequently exceeding
10 µg m-3. In contrast, the “M1–10 without
LRT” data shows a rather modest seasonal cycle with average concentrations
around 6–7 µg m-3 during the dry season (i.e., broad maximum
spanning August–October) and around 4 µg m-3 during the wet
season (i.e., with a minimum in April). In the absence of African LRT
influence, the coarse mode mostly comprises bioaerosols from local/regional
sources (see also Pöschl et al., 2010; Huffman et al., 2012).
Accordingly, Fig. 4e indicates that the Amazonian atmosphere contains a
rather constant coarse mode background, in which bioaerosols likely account
for a dominant fraction. As possible explanation for this (modest)
seasonality of the “M1–10 without LRT” background, we suggest
that this could be determined by (i) differences in aerosols scavenging
frequency (see seasonality of PBT), (ii) a certain fraction of
coarse mode particles originating from biomass burning plumes, and/or
(iii) different bioaerosol emissions patterns and strength in the wet vs. dry
season.
Seasonally averaged aerosol concentrations<?xmltex \hack{\break}?> and size distributions
Figure 5 and Table 2 provide a statistical summary of selected aerosol
concentrations, resolved by season. Figure 5a and b show the seasonal levels
of Ntotal and MBCe, reflecting the characteristic
biomass burning driven seasonal patterns: the
wet season shows clean background concentrations (i.e., median with
interquartile range, IQR (25th–75th percentiles):
Ntotal = 283 (197–420) cm-3 and
MBCe = 0.02 (0.02–0.04) µg m-3), whereas
highest concentration levels occur in the dry season (i.e.,
Ntotal = 1337 (1021–1776) cm-3 and
MBCe = 0.30 (0.21–0.46) µg m-3). The
transitions periods represent an intermediate state in between these extremes
(i.e., Ntotal = 663 (448–963) cm-3 and
MBCe = 0.10 (0.05–0.20) µg m-3). During the
LRT season, we observed a clear MBCe enhancement in comparison to
the wet season background (i.e., MBCe = 0.14
(0.05–0.24) µg m-3 vs. MBCe = 0.02
(0.02–0.04) µg m-3), due to the smoke fraction in the
advected African LRT plumes. Remarkably, the Ntotal levels for
wet vs. LRT episodes show no statistically significant difference. This
suggests that MBCe is a sensitive indicator to discriminate
near-pristine episodes and periods that are influenced by long-range
transported African pollution, whereas the use of Ntotal as a
pollution tracer may be misleading (see also Pöhlker et al., 2017).
The statistics of the multi-year OPS data is presented as number and mass
concentrations for two size ranges: the range from 0.3 to 1 µm in
Fig. 5c and d covers the large-particle tail of the accumulation mode and,
thus, to a certain extent follows the biomass burning seasonality, similar to
Ntotal and MBCe. Note that the parameters, which are
sensitive to biomass burning pollution (i.e., Ntotal,
MBCe, N0.3–1, M0.3–1) generally show a
wide range of statistical scattering (see large IQR), which can be explained
by the fact that dry season aerosol time series are characterized by a
sequence of high-concentration peaks due to the plume-wise advection of
biomass burning emissions (see Pöhlker et al., 2017; Saturno et al.,
2017a). In contrast, the range from 1 to 10 µm in Fig. 5e and f
represents the coarse mode seasonality, which differs from the biomass
burning patterns. The N1–10 and M1–10 levels show a
modest increase from the wet season (N1–10=0.3
(0.2–0.5) cm-3, M1–10=3.5
(2.2–5.4) µg m-3) over the transition periods
(N1–10=0.7 (0.4–1.0) cm-3, M1–10=4.9
(3.4–6.5) µg m-3) to the dry season (N1–10=1.1 (0.8–1.4) cm-3, M1–10=6.4
(4.9–7.8) µg m-3). The highest concentrations for
N1–10 and M1–10 levels clearly occurred during
African LRT influence (N1–10=1.5 (0.6–2.8) cm-3,
M1–10=9.1 (5.2–14.2) µg m-3).
Average aerosol number and mass concentrations for the size ranges
of 0.3–1.0 µm, 1.0–10.0 µm, 0.3–10.0 µm, and
different seasons in the period of February 2014–April 2017: (i) wet season
excluding long-range transport (LRT) episodes, (ii) LRT episodes,
(iii) transition periods, (iv) dry season, and (v) average of the entire data
period (see Sect. 2.10). Total aerosol number concentration (> 4 nm,
CPC-based) and black carbon (BCe) mass concentrations
(MAAP-based) are included for comparison. All results are reported as mean,
median, and one standard deviation (SD). The OPS-retrieved mass
concentrations were calculated for three different densities (ρ = 0.85; 1.0 and 1.2 g cm-3). For the LRT episodes, also ρ = 2.0 g cm-3 was taken into account. The mass concentrations for
the different densities do not scale linearly since the particle loss
correction according to Sect. 2.3 has been implemented. Refer to Table S2 for
corresponding IQR data.
Aerosol number concentrations
Season
Density
N0.3–1 [cm-3]
N1–10 [cm-3]
N0.3–10 [cm-3]
N1–10/N0.3–10 [%]
Ntotal [cm-3]
[g cm-3]
Mean
Median
SD
Mean
Median
SD
Mean
Median
SD
Mean
Median
Mean
Median
SD
Wet
1.0
6.11
4.96
5.06
0.42
0.33
0.34
6.53
5.32
5.28
6.4
6.2
336
285
209
LRT
23.16
15.76
34.54
2.03
1.47
1.87
25.19
17.56
35.50
8.1
8.4
347
302
234
Trans
31.83
15.20
66.71
0.81
0.65
0.75
32.65
16.01
67.25
2.5
4.1
790
666
547
Dry
70.69
57.80
57.60
1.15
1.10
0.81
71.85
58.92
58.13
1.6
1.9
1508
1339
785
All
32.95
23.43
40.98
1.10
0.89
0.94
34.06
24.45
41.54
4.6
5.1
745
648
444
Aerosol mass concentrations
Season
Density
M0.3–1 [µg m-3]
M1–10 [µg m-3]
M0.3–10 [µg m-3]
M1–10/M0.3–10 [%]
MBCe [µg m-3]
[g cm-3]
Mean
Median
SD
Mean
Median
SD
Mean
Median
SD
Mean
Median
Mean
Median
SD
Wet
0.85
0.17
0.14
0.14
3.30
2.83
2.21
3.47
3.03
2.26
95.1
93.5
0.02
0.01
0.03
1.00
0.20
0.16
0.16
4.04
3.47
2.72
4.25
3.70
2.78
95.1
93.8
1.20
0.24
0.19
0.19
5.21
4.45
3.54
5.45
4.73
3.61
95.6
94.2
LRT
0.85
0.74
0.63
1.15
10.94
8.78
8.78
11.82
9.56
10.08
92.6
91.8
0.17
0.13
0.15
1.00
0.87
0.63
1.15
11.28
9.05
9.05
12.16
9.84
10.37
92.8
92.0
1.20
1.04
0.76
1.39
14.26
11.45
12.46
12.75
10.34
10.91
93.2
92.3
2.00
1.74
1.26
2.32
38.72
31.06
35.94
40.48
32.66
36.76
95.7
95.1
Trans
0.85
0.81
0.41
1.71
4.29
4.02
2.80
5.11
4.56
3.82
84.0
88.1
0.17
0.10
0.20
1.00
0.95
0.47
2.01
5.24
4.90
3.46
6.19
5.56
4.62
84.7
88.1
1.20
1.14
0.58
2.41
6.76
6.30
4.50
7.90
7.10
5.83
85.6
88.7
Dry
0.85
1.71
1.37
1.56
5.29
5.20
2.19
7.00
6.79
3.23
75.6
76.6
0.35
0.30
0.20
1.00
2.01
1.61
1.84
6.47
6.36
2.69
8.48
8.25
3.88
76.3
77.1
1.20
2.41
1.93
2.21
8.32
8.15
3.47
10.73
10.44
4.84
77.5
78.1
All
0.85
0.86
0.64
1.14
5.95
5.21
4.18
6.85
5.99
4.85
86.8
87.5
0.18
0.14
0.15
1.00
1.01
0.72
1.29
6.76
5.95
4.68
7.77
6.84
5.41
87.2
87.7
1.20
1.21
0.86
1.55
8.04
7.11
5.47
9.21
8.15
6.30
88.0
88.3
Seasonal variation of coarse mode aerosol size distributions, based
on OPS data from February 2014 until April 2017: (a) number size
distributions, dN/dlogD, (b) surface size
distributions, dS/dlogD, and (c) volume size
distributions, dV/dlogD. The
dS/dlogD and dV/dlogD
distributions were calculated based on dN/dlogD
assuming spherical particles (see Sect. 2.2). Markers represent median values
with error bars as 25th and 75th percentiles. (d) Volume size
distribution representing actually advected LRT aerosols at ATTO (median LRT dV/dlogD –
median wet dV/dlogD distributions from c) and
mass size distributions dM/dlogD for dust conditions
during CLAIRE-98 experiment in Balbina, Brazil, as specified in Formenti et
al. (2001). For the CLAIRE-98 distributions, the results from three filter
samples taken between 24 and 27 March 1998 were averaged. The size range of
the OPS covers both the coarse mode (1–10 µm) and the large
diameter tail of the accumulation mode (0.3–1 µm). In terms of the
coarse mode, quantitative information on its location, intensity, and shape
were obtained. The accumulation mode information is semi-quantitative, since
only a part of the mode is covered, which suffices to estimate the overall
accumulation mode strength. For definition of the seasons refer to
Sect. 2.10. Particle size distributions from the Amazonian studies by Huffman
et al. (2012) and Whitehead et al. (2016) were included for comparison.
![]()
Coarse mode number and mass concentrations from selected earlier
studies in the Amazon and other forested locations.
Location and year of measurement
Aerosol size range [µm]
Instrument/technique
Season
N [cm-3]
M [µg m-3]
Reference
Comments
(Mean ± 1 SD)
Primary rain forest site, Reserva Bio-
∼ 2–10
Gravimetric filter
Dry season (Sep–Oct)
–
6.5 ± 6.1
Artaxo et al. (2002)
Comprehensive LBA study in
logica Jarú, Rondônia, Brazil, 1999
analysis
Wet season (Jan–May)
–
5.1 ± 2.6
biomass burning region on the
Pasture site, Rondônia, Brazil, 1999
Dry season (Sep–Oct)
–
17.8 ± 11.7
effect of fires on aerosol load
Wet season (Jan–May)
–
5.7 ± 3.1
Primary rain forest site, Reserva Biologica Jarú, Rondônia, Brazil, 1999
∼ 2–10
Gravimetric filter analysis
Dry season (Sep–Oct)Wet season (Apr–May)
––
6.6 ± 3.03.8 ± 1.3
Guyon et al. (2003)
Systematic analysis of Amazonian aerosol composition in wet and dry season
Primary rain forest site, Balbina site, Amazonia, Brazil, 2001
∼ 2–10
Gravimetric filter analysis
Transition period wet to dry season (Jul)
–
3.9 ± 1.4
Graham et al. (2003)
Short-term study on diurnal aerosol variability
Primary rain forest site, ZF2 site,
∼ 2–10
Gravimetric filter
Dry season
–
4.4 ± 2.4
Artaxo et al. (2013b)
4-year study at remote central
Amazonia, Brazil, 2008–2011
analysis
Wet season
–
5.0 ± 2.0
Amazonian site
Rain forest, Amazon Basin (Rondonia)
∼ 2–10
Gravimetric filteranalysis
Dry season (Jun–Dec)Wet season (Jan–May)
––
10.2 ± 9.08.8 ± 5.3
Artaxo et al. (2013b)
Multi-year study (2009–2012) in Amazonian region that is subject to strong land-use change and biomass burning
Primary rain forest site, ZF2 site, Amazonia, Brazil, 2008
∼ 0.7–10
UV-APS
Wet season (Feb–Mar), “high dust” conditions
0.93
3.89
Huffman et al. (2012b)
FBAP analysis during AMAZE-08
Wet season (Feb–Mar), “low dust” conditions
0.26
1.63
Wet season (Feb–Mar), campaign average
0.55
2.49
Primary rain forest site, ZF2 site, Amazonia, Brazil, 2013
> 1
WIBS
Transition period wet to dry season (Jul)
0.46
–
Whitehead et al. (2016)
Short-term campaign of FBAP
Boreal forest site, SMEAR2 station,
∼ 0.7–10
UV-APS
Spring
0.43
–
Schumacher et al. (2013)
Long-term (> 1 year) FBAP study
Hyytiälä, Finland
Summer
0.45
–
in boreal forest environment
Fall
0.41
–
Winter
0.47
–
Campaign average
0.44
–
Semi-arid forest site, Manitou
∼ 0.7–10
UV-APS
Spring
0.73
–
Schumacher et al. (2013)
Long-term (> 1 year) FBAP study
experimental forest, Colorado, USA
Summer
0.44
–
in semi-arid forest environment
Fall
0.28
–
Winter
0.20
–
Campaign average
0.41
–
To discuss our observed N1–10 and M1–10
concentrations in the context of previous measurements, we summarized the
results from related studies in Table 3. A certain number of previous studies
from the central and southern Amazonian region reported coarse mode
concentrations, which agree well with the N1–10 and
M1–10 levels in this work (Artaxo et al., 2002, 2013b; Graham et
al., 2003; Guyon et al., 2003; Huffman et al., 2012; Whitehead et al., 2016).
For comparison, we added a few studies from other locations and ecosystems
(i.e., boreal and semi-arid forest sites) to Table 3, which suggest that the
coarse mode (background) concentrations in different forested ecosystems are
remarkably similar and range around 0.5 cm-3 (Schumacher et al., 2013).
Figure 6 illustrates the seasonal differences in the aerosol size
distributions obtained from the multi-year OPS measurements. Similar to the
number and mass concentrations
in Fig. 5e and f, we observed the strongest coarse mode during African LRT
episodes, followed by the dry season, the transition periods, and lastly the
wet season. In addition to these concentration trends, the coarse mode also
reveals seasonal characteristic shapes (see surface and volume size
distributions in Fig. 6b and c). Under wet season conditions, the coarse mode
maximum is shifted towards large diameters (∼ 2.8 µm in the
surface and ∼ 4.2 µm in the volume size distributions) and
the entire mode can be characterized as a broad monomodal distribution. In
contrast, during the dry season, the coarse mode shape is clearly different.
It has a multimodal appearance and the strongest, rather narrow peak is
located at ∼ 1.7 µm (surface size distribution) and
∼ 2.0 µm (volume size distribution), respectively. Towards
larger diameters, a pronounced shoulder indicates the presence of one or two
further modes. During the transition periods, the coarse mode resembles a
mixture of the wet and dry season size distributions. The coarse mode shape
during Saharan dust influence appears monomodal with its maximum at
∼ 2.0 µm (surface size distribution) and
∼ 2.4 µm (volume size distribution), respectively. Our best
approximation for the volume size distribution of the actually advected LRT
aerosols without the persistent biogenic background in the coarse mode is
shown in Fig. 6d and has been obtained by subtracting the LRT and wet-season
volume size distributions (see Fig. 6c). This ”LRT-wet” volume size
distribution resembles a dust mass size distribution based on data from the
CLAIRE-98 experiment in Balbina, Brazil (for details see Formenti et al.,
2001). Here, the quantified concentrations of the dust marker cations
aluminum (Al), silicon (Si), calcium (Ca), magnesium (Mg), and iron (Fe) as
reported in Formenti et al. (2001) were converted into their corresponding
oxides (i.e., Al2O3, SiO2, CaO, MgO, Fe2O3) to
obtain an overall mass size distribution of the advected dust aerosol, as
shown in Fig. 6d. The comparison underlines that the volume size distribution
of the advected LRT aerosols shows a characteristic peak between 2 and
3 µm.
For comparison, we added coarse mode size distributions from two previous
Amazonian studies to Fig. 6. It has to be kept in mind that optical particle
sizing may deviate from geometric and/or aerodynamic sizing approaches (see
also Sect. 2.2). Huffman et al. (2012) conducted a multi-week measurement in
the central Amazon during the wet season using an ultra-violet aerodynamic
particle sizer (UV-APS) to probe wet season conditions with African dust
influence. The resulting campaign average number and volume size
distributions agree well with our observations for particle sizes
< 3 µm (Fig. 6a and c). For diameters > 3 µm, the OPS-based results shows much
higher particles abundances than the UV-APS-based
distributions. This deviation can
probably be explained by a combination of different reasons. An important
aspect could be that optical particle sizing tends to oversize aerosol
particles relative to aerodynamic particle sizing (i.e., UV-APS) (Reid et
al., 2003b). Moreover, optical sizing is often associated with a broadening
of the distributions. Both of these tendencies are consistent with our
observations in Fig. 6a and have also been reported previously for the
Amazonian coarse mode (Martin et al., 2010a). However, a systematic and
long-term comparison of optical and aerodynamic particle size properties of
Amazonian aerosols is subject of ongoing work. The second study for
comparison was conducted by Whitehead et al. (2016) using a WIBS (optical
sizing) to probe several weeks of the transition period from wet to dry
season (Fig. 6a). The WIBS data agree well with our observations for the
particle size range > 2 µm. Below 2 µm, the detection
efficiency of the WIBS drops, which probably explains the deviation between
WIBS and OPS in this range.
Comparison of experimental data and GEOS-Chem model results
For the wet season and LRT episodes of the years 2014 and 2015, a comparison
of the experimental data (i.e., M0.3–10) and GEOS-Chem model
results along the lines of the study by Wang et al. (2016) was conducted.
Measured and modeled results show a rather accurate agreement, particularly
for the timing and mass concentrations of the LRT episodes. The corresponding
time series and a bivariate regression fit are shown in Figs. S7 and S8. The
good agreement indicates that the relevant factors, controlling the dust
transport into the basin, are accurately covered by the model. Based on this
convincing model result validation, we extracted further data products from
the model runs, which highlight relevant atmospheric and ecological aspects
of the dust deposition in the Amazon Basin.
GEOS-Chem model results representing: (a) dust aerosol
deposition flux and (b) contribution of wet dust deposition (rain
out and wash out) relative to total deposition flux. Both data products were
calculated from January to April 2014. The location of the ATTO site is
represented by a red marker.
![]()
The effective dust deposition, which introduces essential micro- and
macronutrients onto the low-fertility Amazonian soils, is a relevant aspect
from an ecological perspective (e.g., Swap et al., 1992; Okin et al., 2004;
Bristow et al., 2010; Rizzolo et al., 2017). Figure 7a displays a map of the
modeled dust deposition flux into the basin. For the Amazon region and
during the time period January to April 2014, the model predicts average
deposition fluxes from 5 to 500 ng m-2 s-1, which is in good
agreement with previous studies, e.g., by Yu et al. (2015), who indicated a
7-year-average deposition flux in the range from 25 to
160 ng m-2 s-1 for the entire Amazon area. Note that the dust
deposition occurs heterogeneously across the basin. The highest deposition
fluxes (about 100–500 ng m-2 s-1) can be found in the NE of the
basin (i.e., the Guiana shield and the region around the Amazon River delta),
whereas deposition fluxes decrease towards the southwest.
According to the model, the belt within the deposition gradient that includes
the ATTO region (shown in yellow), received an average effective dust
deposition of about 50–100 ng m-2 s-1 during the 2014 dust
season (i.e., January–April 2014). Since January to April represent the LRT
core months and include most of the LRT episodes, we assume that most of the
dust deposition occurs within this time window. Accordingly, the deposited
mass from January to April is regarded as a good representation of the total annual
deposition. Thus, based on the average deposition flux, we obtained a total
annual deposited dust mass of about 0.5–1 g m-2 or
5–10 kg ha-1 for 2014, respectively. This is in good agreement with
the total deposited mass of 8–50 kg ha-1 a-1 reported by Yu et
al. (2015) as well as the study by Swap et al. (1992), in which the authors
estimate that the total mass of introduced dust “may amount to as much as
190 kg ha-1 a-1” in the northeastern basin. We propose that the
results obtained here for the year 2014 can be regarded as representative for
a typical dust deposition scenario in the Amazon region, since 2014 was
generally an “average” year without pronounced precipitation and
circulation anomalies (Pöhlker et al., 2016, 2018). Moreover, the four
LRT seasons (2014–2017) analyzed in this study show generally similar trends
and patterns. Given that the atmospheric input of essential dust-related
nutrients plays a crucial role in rain forest ecology, differences in forest
structure and diversity (e.g., total biomass) may reflect the spatial
difference in dust deposition as shown in Fig. 7a. This link between
atmospheric nutrient input and forest ecology has already been subject of
previous studies, however, it requires further research to answer various
open questions.
Temporal trend of the slopes from correlations between
MBCe and M1–10 from systematic bivariate regression
fit analysis during the wet season period. Bivariate regression fits (with
offset) have been performed every day during the wet season months of February to
May for the years 2014 to 2017. The results have been filtered and only those
slope data points from regression fits with a clear correlation (i.e., with a
correlation factor R2 > 0.5) are shown here. The error bars
represent the standard error of the slope. To emphasize the overall trend, an
exponential fit has been added with its 1σ prediction band.
![]()
Meteorological and aerosol time series during the wet season 2014
case study. (a) Colored markers represent time periods of relevant
intensive observation activities during 2014 case study.
(b) Frequency of occurrence, fBT,clusters, of 15 back
trajectory clusters as displayed in Fig. S4 (with identical color coding).
(c) Image plot of aerosol surface size distribution for the size
range from 80 nm to 10 µm. (d, left axis) Time series of
the aerosol scattering coefficients, σsp, at three different
wavelengths. (d, right axis) Equivalent black carbon mass
concentration, MBCe, calculated assuming a mass absorption cross
Section MAC of 11.4 m2 g-1 during the wet season. (e,
left axis) Satellite-retrieved aerosol optical depth at 550 nm,
area-averaged over offshore region of interest (ROIoff, see
Fig. S4). The AODROI,off time series represents the average of
the MODIS data sets from the satellites Aqua and Terra. (e, right
axis) HYSPLIT-retrieved accumulated precipitation, PBT, along the
trajectory tracks. (f) Aerosol mass concentrations in the coarse
mode 1–10 µm (M1–10). Time series represent hourly
averages, except fBT,clusters, AODROI,off, and
PBT. Gray vertical bands mark episodes when Saharan LRT aerosol
was measured at the ATTO site (see Table 1). The fBT,clusters,
AODROI,off, and PBT time series are shown as daily
averages. The image plot of the aerosol size surface distribution as well as
the σsp, MBCe, and OPS mass concentrations time
series are shown as daily averages.
![]()
As an additional aspect, Fig. 7b emphasizes that the deposition of dust into
the basin is predominantly driven by wet processes (i.e., rain out and wash
out), which is consistent with previous aspects in this study (e.g., the
rain-related pulse-wise modulation of dust plume transport in the basin).
Swap et al. (1992) similarly emphasized that in the Amazonian wet season
atmosphere, “precipitation scavenging is the principal removal mechanism of
Saharan dust”. This result further underlines that potential changes in
precipitation patterns in the Amazon region will also impact the dust
deposition into the ecosystem.
Quantification of black carbon fraction in the African LRT
plumes
A characteristic feature of the analyzed LRT plumes arriving in the Amazon
Basin is their “smokiness”, due to
the fact that substantial amounts of pyrogenic aerosols from fires in West
Africa are mixed into the Saharan dust aerosols as illustrated in Fig. 1.
This is a result of the fact that the biomass-burning season in West Africa
coincides with the period of frequent LRT to the Amazon Basin. The presence
of pyrogenic aerosols resulted in high BC concentrations during the LRT
episodes in comparison to the wet season background as shown in Fig. 5. Here,
we analyze the relative BC fractions and their variability in more detail.
For most LRT plumes, a positive correlation between M1–10 and
MBCe was found, underlining the joint arrival of BC and dust
aerosols in the mixed plumes. Selected examples of M1–10 vs.
MBCe scatter plots can be found in Fig. S9 with linear bivariate
regression fits (with offset), in which the slopes represent the BC fraction
relative to the dust aerosol mass. Based on the data shown in Fig. S9, slopes
of 0.009 ± 0.001 and 0.016 ± 0.015 (mean slope ± 1 SD)
were found.
Diurnal cycles in selected aerosol and meteorological parameters
during the wet season without major dust influence vs. LRT episodes within
the time frame shown in Fig. 9. (a, b) Image plots of aerosol size
surface distribution for the size range from 0.8 to 10 µm.
(c, d) Coarse mode aerosol mass concentration M1–10.
(e, f) Micrometeorological parameters, temperature, RH, and incoming
short-wave solar radiation.
![]()
Generally, our analysis showed that the relationship between
M1–10 and MBCe is characterized by a comparatively
high variability due to the spatiotemporal heterogeneity of the LRT plumes
depending on their source regions and transport paths from Africa to South
America. Along these lines and in order to analyze the temporal trends of the
smoke fraction in the arriving plumes, we conducted a systematic linear
regression analysis based on the entire dataset analyzed here (i.e., the time
frame February to May for the years 2014 to 2017). The corresponding results,
which are shown in Fig. 8, clearly indicate that the LRT events in the early
wet season (i.e., February) comprise substantially higher smoke fractions
(reaching up to 0.05) than the LRT events in the later wet season (i.e.,
April), when the mean slope converges against ∼ 0.005. This observation
is consistent with concurrently decreasing fire intensities in Africa as
described by Barbosa et al. (1999) and supports lidar-based findings from the
wet season of 2008 (Baars et al., 2011).
Case studies with detailed analysis of specific<?xmltex \hack{\break}?> LRT events
So far, we have presented the overall and long-term
patterns in coarse mode variability
and the pulse-wise Saharan dust advection. In the following sections, we zoom
into selected time periods and show specific case studies to highlight
relevant short-term aspects and observations beyond what has been discussed
so far. In particular, we will focus on the LRT episodes of the years 2014
and 2015.
The wet season 2014, which includes seven LRT episodes (2014_1 to
2014_7, see Table 1), is of particular interest since it overlaps with
several intensive observation activities (Fig. 9a): (i) the first IOP of the
GoAmazon2014/5 campaign, which targeted clean wet season conditions, took
place from 1 February to 31 March 2014 (Martin et al., 2017). The
GoAmazon2014/5 IOPs are subject of intensive analysis of various facets of
atmospheric composition (see
https://acp.copernicus.org/articles/special_issue392.html, Allan et al.,
2015). According to our analysis, the IOP1 contained five LRT episodes (i.e.,
2014_1 to 2014_5, see Table 1), which interrupted the Amazonian
background conditions with 37 of the 59 IOP1 days being classified as LRT
impacted. Accordingly, LRT condition are not an exceptional but rather the
predominant atmospheric state during this time period. (ii) Wang et
al. (2016) conducted an in-depth GEOS-Chem modeling study on light-absorbing
aerosols with African LRT plumes being an important source of pyrogenic and
dust aerosols. (iii) The CCN studies by Pöhlker et al. (2016, 2017) cover
three LRT episodes in 2014 and analyze the CCN-relevant properties of the
advected aerosol population (i.e., the LRT episode 2015_7 is discussed in
detail there). (iv) Intensive aerosol sampling targeting LRT conditions in
February and March 2014 was conducted and the corresponding results using
microspectroscopic techniques are currently prepared for a follow-up
manuscript on the morphology, mixing state, and composition of the LRT
aerosol population.
For comparison with the conditions in 2014 as shown in Fig. 9, an analogous
overview for the LRT episodes in 2015, including five LRT episodes (2015_1
to 2015_5, see Table 1) can be found in Fig. S10. Generally, Figs. 9 and
S10 – which display the online aerosol data at hourly resolution in contrast
to daily resolution in Fig. 2 – clearly show the characteristic conditions
of efficient dust transport towards ATTO as discussed in detail in Sect. 3.1:
All observed LRT episodes, represented by M1–10, are associated
with elevated AODROI,off levels, relative minima in
PBT, and a predominance of the back trajectory clusters NE2 and
NE3.
MODIS-derived AOD data showing Saharan dust outbreak, transatlantic
passage, and intrusion into the Amazon Basin in the beginning of April 2014.
Selected orbits of the CALIPSO spacecraft on 5 and 10 April are shown
in (a, d). Corresponding CALIOP lidar profiles transecting the dust
plume are displayed in Fig. 12. The gray areas represent pixels with no
satellite data for the corresponding time periods.
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In addition to the multi-day LRT peaks in coarse mode abundance, a pronounced
diurnal pattern can be recognized in the coarse-mode-relevant time series
(i.e., Fig. 9c and f) as well as in MBCe and σsp. In order to characterize these observations in more detail, we
extracted diurnal cycles for two contrasting states: wet season conditions
without LRT influence (Fig. 10a) and for LRT episodes only (Fig. 10b). In the
absence of LRT influence, our results are consistent with previous
observations by Huffman et al. (2012) and Whitehead et al. (2016), showing a
maximum in coarse mode abundance during the night (i.e., around
01:00–02:00 LT) and a minimum in coarse mode abundance during afternoon
hours (i.e., around 12:00–13:00 LT). It has been suggested by Huffman et
al. that these trends are driven by a combination of variable dispersal of
biological aerosols, which is “strongly tied to environmental variables,
such as solar radiation, temperature, and moisture”, as well as the
oscillating height of the atmospheric boundary layer that concentrates local
emissions during night and dilutes them convectively during the day. Along
these lines, Fig. 10a clearly illustrates coherent diurnal patterns of
temperature, relative humidity, radiation, and M1–10.
Remarkably, Fig. 10a and the results by Huffman et al. showed consistently a
secondary maximum at 08:00 LT, which could indicate increased sporulation
rates due to the onset of solar radiation and continuously high relative
humidity levels. The specific responses of PBAP emission mechanisms to
micrometeorological conditions are subject of an ongoing analysis. In
contrast to this scenario, the diurnal pattern during LRT episodes shows a
different trend as shown in Fig. 10b. Here, the highest coarse mode abundance
occurred around ∼ 12:00 LT, coinciding with the maximum in incoming
radiation. This observation suggests that the intrusion of LRT aerosols into
the near-surface boundary layer occurred via convective downward mixing from
higher altitudes, where the transport of the plumes mostly takes place. After
sunset, the M1–10 levels decrease instantaneously, suggesting an
efficient deposition of the LRT aerosol load to surfaces in the canopy space.
As a further step, we zoomed into two particular LRT episodes for a detailed
analysis using satellite-based remote sensing data: (i) the event 2014_7
from 8 to 14 April 2014 and (ii) the event 2015_5 from 2 and
10 April 2015. For the event 2014_7, Figs. 11 and 12 provide a remote
sensing characterization of the corresponding dust plume. The sequence of AOD
maps in Fig. 11a–d shows the temporal evolution of the African dust outbreak
as it passed over the Atlantic Ocean (4 to 5 April), arrived at the
northeastern coast of South America (6 to 7 April), and traveled (deeply)
into the Amazon Basin(8 to 11 April). Note that the AOD data indicates
a plume arrival on 8 April, which agrees very well with our in situ
observation of the actual plume arrival at ATTO as shown in Fig. 9.
Figure 11c shows that the plume impacted a large area including southern
Venezuela, Guyana, Suriname, French Guiana, and northern Brazil. Accordingly,
the detailed presentation of the ATTO measurements for this particular event
can be regarded as characteristic for atmospheric conditions under LRT
influence in a comparatively large area of the northern Amazon Basin. For
comparison, the corresponding AOD maps for the 2015_5 event can be found
in Fig. S11.
In addition to the MODIS characterization, Fig. 12 presents lidar data from
two CALIPSO overpasses that characterized the dust plume in a rather young
state during its transatlantic passage (on 5 April 2014) and at a later stage
as it reached the ATTO region (on 10 February 2014). The CALIPSO data for the
overpass on 5 April probed the dust plume in the middle of the Atlantic Ocean
and emphasizes its large horizontal extent from 20∘ N towards the
equator (Fig. 12a, c, e). It further illustrates that the aerosol layer is
lofted above the marine boundary layer with a vertical extent up to altitudes
around 4–5 km and a certain degree of stratification. Note in this context
that the transatlantic dust transport has been found to characteristically
occur in lamella-like stratifications (Ansmann et al., 2009). Although the
CALIOP aerosol subtype categorization (Fig. 12e, f) is showing a thin marine
layer close to the surface, shallow moist convection likely facilitated the
dust layer also to reach down into the marine boundary layer. Further, the
aerosol subtype categorization confirms that the Saharan dust outbreaks
during this time of the year are typically mixed with substantial amounts of
pyrogenic aerosol, in agreement with Figs. 8 and 9 and the related
discussion. The CALIPSO overpass on 10 April shows a lidar profile relatively
close to the ATTO site (Fig. 12b, d, f). In the region of the ITCZ belt with
its deep convective clouds, the signal is completely attenuated (i.e.,
0–5∘ S). However, the cloud-free areas show the presence of a
compact and relatively well mixed dust layer up to altitudes of 2–3 km, in
agreement with Ansmann et al. (2009). For comparison, an analogous CALIPSO
characterization for the LRT episode 2015_5 can be found in Fig. S12,
which shows consistent overall trends. Further note that ceilometer
measurements at the ATTO site in 2015 confirm that the LRT plumes arrive in
the ATTO region as compact and mixed layers below 3 km (see Fig. S13).
CALIOP lidar profiles of the African dust plume from 5 and
10 April 2014. The corresponding satellite orbits are shown as overlay with
the MODIS AOD maps in Fig. 11. Red markers in panel (d) show
position of the ATTO site and the northeast South American coast.
![]()
The first prerequisite for effective dust transport to ATTO is the arrival
and availability of a dust plume at the South American coast (see Sect. 3.1).
For the 2014_7 episode, the remote sensing products shown above indicate
the plume arrival at the coast on 6 April. The subsequent effectiveness and
the role of wet deposition during its transport over land towards ATTO is
illustrated in Fig. 13, presenting a sequence of the average precipitation
patterns and wind fields at the 925 hPa level in the respective regions of
interest. On 2/3 April, we find an almost closed rain band clearly
illustrating the position of the ITCZ, which effectively prevents dust
transport further south. In the course of the following days, the rain band
became disturbed and moved slightly south, leading to a decrease in
precipitation over French Guiana, Suriname and the northeastern Amazon
Basin. Finally, on 9 and 10 April, precipitation stopped in the NE fetch of
ATTO and the NE trade wind circulation established, opening the door for
effective advection of dust towards the ATTO site. This interplay of the
availability of dust, the timing of transport, and minimal wet deposition
underlines the episodic but mesoscale character of dust intrusions into the
northeastern basin (Swap et al., 1992). For comparison, an analogous and
consistent characterization of the 2015_5 episode can be found in
Fig. S14.
Wind data and precipitation daily averages from NCEP reanalysis data
at a spatial resolution of 2.5∘ from 2 to 13 April 2014 corresponding
to 2014_7 period shown in Fig. 11.
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Chemical characterization of LRT aerosols
In this section, we present an analysis of the chemical composition of the
LRT aerosols that are advected to the ATTO site. This analysis is based on
EDXRF data, which are available from the ZF2 site for the LRT season 2015 (see details in Sect. 2.4).
Nine multi-day filter samples have been selected that best represent the
conditions with and without LRT influence (see Fig. S10). The results are
summarized in Fig. 14 for LRT episodes vs. conditions in the absence of
strong LRT plumes (called non-LRT), both for D < 2.5 µm and
2.5 < D < 10 µm. Figure 14 quantifies the elemental
contributions to the total collected mass on the filters as well as the mass
concentrations of the individual elements. It clearly emerges that the
relative fractions and mass concentrations of the dust-related crustal
elements Si, Al, Fe, Ti, and Ca as well as the sea salt-related elements Na,
Cl, and Mg are – expectedly – higher for LRT than non-LRT conditions. For
instance, a total Si mass concentration of ∼ 500 ng m-3 (sum of
D < 2.5 and 2.5 < D < 10 µm size fractions) was
observed under LRT influence, whereas ∼ 35 ng m-3 were found
under non-LRT conditions. Similarly, total Na mass concentrations went up to
∼ 130 ng m-3 under LRT influence, in contrast to non-detectable
amounts for the non-LRT case. Moreover, S and K concentrations are enhanced
under LRT influence. Note in this context that the EDXRF-retrieved sulfate
concentrations of ∼ 440 ng m-3 (for D < 2.5 µm;
obtained from conversion of S into SO42- mass) under LRT
influence agrees well with the aerosol mass spectrometry-based LRT sulfate
concentrations of ∼250 ng m-3 (for D < 1 µm) as
reported in Pöhlker et al. (2017). Interestingly, only minor amounts of
Cl were found in the D < 2.5 µm fraction, which could
indicate a strong processing of small NaCl particles during atmospheric
transport and an almost full replacement of the Cl- anion by
NO3- and/or SO42- anions, which is in agreement
with the comparatively high sulfate concentrations in the same size fraction
(Laskin et al., 2012). As a further general trend, the fraction of the
aforementioned inorganic elements tends to be higher in the particle size
fraction D < 2.5 µm compared to
2.5 < D < 10 µm. The lighter elements with atomic numbers
< 11, which account for the remaining part of the total mass, can
predominantly be attributed to C, N, and O. For all four cases, the CNO
contribution accounts for large fractions, ranging from about 71 % (LRT
case for D < 2.5 µm, Fig. 14a) to 97 % (non-LRT case for
2.5 < D < 10 µm, Fig. 14d). Under non-LRT conditions, the
elements K, P, and S, which are typically associated with biogenic particles,
prevail besides the dominant CNO fraction. Generally, the results in Fig. 14
agree very well with previous studies on the Amazonian aerosol composition,
where detailed discussions can be found (Lawson and Winchester, 1979; Talbot
et al., 1990; Graham et al., 2003; Guyon et al., 2003). Note in the context
of the high CNO fraction that coarse mode particles in the Amazon are
typically coated by SOA (Pöschl et al., 2010).
Mean relative mass fractions (in percent) and elemental mass
concentrations (in ng m-3, in parenthesis) of aerosol particles in the
size fractions > 2.5 and < 2.5 µm for LRT episodes vs.
periods without LRT influence (called non-LRT here). The sampling periods of
the 5 LRT filters and the 4 non-LRT filters are specified in Sect. 2.4 and
plotted in Fig. S10. The average elemental fractions are quantified relative
to the average deposited mass on the LRT vs. non-LRT filters, respectively.
The EDXRF analysis allows to quantify the masses of elements with atomic
numbers larger 10 (i.e., starting with Na). Accordingly, the lighter elements
in the periods 1 and 2 of the periodic table of the elements account for the
remaining mass. The elements C, N, and O represent the predominant
contributors here. The results visualized here are provided in detail in the
corresponding Table S3. If element are marked with *, some data point were
excluded from the analysis since they were below the detection limit
according to Arana et al. (2014) (see also Sect. 2.4).
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The elemental data in Fig. 14 can be regarded as an estimate of the typical
composition of African LRT aerosols, including Saharan dust, marine aerosols,
and smoke, arriving at the ATTO site. This is valuable information as it can
be linked to the retrieved dust deposition fluxes in Sect. 3.3. Accordingly,
the combination of both results allows to obtain deposition fluxes of
individual elements for different regions of the basin (see Fig. 7). This is
particularly relevant for those ecologically important elements that are
regarded as essential micro- and macronutrients for the rain forest
ecosystem, such as Fe, P, S, Ca, Mg, Na, Cl, and others (Swap et al., 1992;
Okin et al., 2004; Rizzolo et al., 2017). For the ATTO region, our estimated
elemental deposition fluxes are summarized in Table 4. This analysis suggests
that the heavier elements with the largest input fluxes are the crustal
elements Si and Al (∼ 410–810 and
∼ 200–410 g ha-1 a-1), followed by sulfur
(∼ 140–270 g ha-1 a-1), and the sea salt elements, Cl and
Na (∼ 130–260 and ∼ 110–230 g ha-1 a-1). For the
ecologically important element Fe, an input of
∼ 120–240 g ha-1 a-1 into the rain forest ecosystem was
estimated. For P, our analysis results in comparatively small input fluxes of
about (∼ 10–20 g ha-1 a-1), since the P abundance is only
slightly enhanced for the LRT (21 ng m-3) in comparison to the non-LRT
case (11 ng m-3). Swap et al. (1992) also provide deposition fluxes
for some selected elements (i.e., Na, K, Cl, P) and ions (i.e.,
NH4+, NO3-, SO42-,
PO43-). Their deposition fluxes are overall comparable to ours,
however, appear to be systematically higher (see Table 4), which is
consistent with their significantly higher estimate for the total deposited
dust mass (Sect. 3.3). It has to be kept in mind that only part of the total
deposited material is soluble and, thus, bioavailable. Accordingly, further
dedicated studies on the bioavailable fractions of key nutrient, along the
lines of the recent study by Rizzolo et al. (2017), will be needed to explore
the link between Saharan dust-related nutrient input and rain forest ecology
in more detail.
Estimated annual deposition of LRT-related elements in the ATTO
region. The results are based on the modeled total dust aerosol deposition
of 5–10 kg ha-1 a-1 as discussed in Sect. 3.3 in combination
with the EDXRF results in Fig. 14. Note that these numbers represent only the
part of the dust deposition gradient in Fig. 7 that includes the ATTO site
(yellow area). For comparison, elemental deposition flux reported by Swap et
al. (1992) are reported as well.
Element
Estimated flux deposition [g ha-1 a-1]
This study
Swap et al. (1992)
Na
110–230
800–3400a/730–2900b
Mg
89–180
–
Al
200–410
–
Si
410–810
–
P
9–17
11–46a/3–39b
S
140–270
140–2340a/400–1040b
Cl
130–260
2500–4900a/1400–5100b
K
110–230
230–870a/410–2300b
Ca
54–110
–
Ti
17–33
–
Mn
6–12
–
Fe
120–240
–
a Dust intrusion estimate. b Precipitation
estimate.
Conclusion
In this manuscript, the long-term variability and properties of coarse mode aerosols in the Amazon rain forest are investigated
based on an extensive data set starting in 2014 at the Amazon Tall Tower
Observatory (ATTO) site.
The coarse mode aerosols originate from different sources, such as direct
emissions of primary biological aerosol particles, marine aerosols,
long-range transport (LRT) of Saharan dust plumes, and a coarse mode
fraction of biomass burning aerosols (Martin et al., 2010b; Huffman et al.,
2012). Therefore, different aspects, such as the seasonal variability of the
background coarse mode properties compared to frequent LRT intrusions are
highlighted.
The complex meso-scale nature of annually re-occurring LRT events in the
Amazon Basin is investigated based on a detailed air mass history cluster
analysis and further remote sensing and in situ data. Tracking a typical dust
layer on its way from the African to the American continent reveals the most
important prerequisites for the efficient advection of LRT aerosols:
(1) arrival and availability of LRT aerosol plumes at the South American
coast, (2) atmospheric circulation in the NE basin and its efficiency in the
transport of dust from the coast towards the ATTO site, and (3) the extent of
wet deposition of the aerosol load en route. Consequently, air mass
trajectories with high average air mass velocity, northernmost tracks, and
lowest integrated precipitation rates overlap best with off-shore areas of
increased dust loading and tend to be the most efficient dust transporters to
the ATTO site region.
In contrast to the sub-micron aerosol fraction, the atmospheric life cycle of
the aerosol coarse mode is not primarily driven by a pollution-related
seasonality. The emission and transport of natural aerosols released and
dispersed on different spatiotemporal scales lead to a rather defined and
surprisingly stable coarse mode mass concentration of
4–7 µg m-3. The seasonal coarse mode number and mass
concentration levels (N1–10 and M1–10) show a
modest increase from the wet season
(N1–10 = 0.42 ± 0.34 cm-3,
M1–10 = 4.04 ± 2.72 µg m-3) over the
transition periods (N1–10 = 0.81 ± 0.75 cm-3,
M1–10 = 5.24 ± 3.46 µg m-3) to the dry
season (N1–10 = 1.15 ± 0.81 cm-3,
M1–10 = 6.47 ± 2.69 µg m-3). During
the wet season, frequent intrusions of LRT aerosols significantly alter the
particle number size distribution and chemical composition. Accordingly, the
highest N1–10 and M1–10 levels clearly occurred
during African LRT influence, leading to average
N1–10 = 2.03 ± 1.87 cm-3 and
M1–10 = 11.28 ± 9.05 µg m-3. During
major LRT events, the coarse mode mass concentration typically increases by
about one order of magnitude, occasionally reaching peak concentrations of
M1–10 = 100 µg m-3.
Under wet season conditions (without LRT), the entire coarse mode can be
characterized as broad monomodal distribution with large mean geometric
diameter (4.2 µm in the volume size distributions). In contrast,
during the dry season, the coarse mode appears to have a multimodal shape
with a strong, rather narrow peak located at about 2.0 µm (volume
size distribution) and pronounced shoulder towards larger particles. The
coarse mode shape during Saharan dust influence shows a monomodal
distribution with its maximum at 2.4 µm (volume size distribution).
A closer look at the diurnal cycling of particle mass concentrations during
wet season conditions (without LRT) reveals a maximum in coarse mode
abundance during the night (i.e., around 01:00–02:00 LT) and a minimum
during the afternoon hours, which is consistent with previous observations by
Huffman et al. (2012) and Whitehead et al. (2016). As suggested by Huffman et
al., these trends are driven by a combination of variable dispersal of
biological aerosols, connected to environmental and meteorological variables,
as well as the oscillating height of the atmospheric boundary layer that
concentrates and dilutes local emissions. In
contrast, the diurnal pattern during LRT episodes shows highest coarse mode
mass concentrations around 12:00 LT, collocated with the maximum in incoming
solar radiation and increasing vertical mixing. This observation suggests
that the intrusion of LRT aerosols into the near-surface boundary layer
occurs via convective mixing with (lofted) aerosol layers at higher
altitudes. After sunset, as soon as less efficient vertical mixing cuts
further supply, an instantaneous decrease in M1–10 levels
suggests efficient deposition of the LRT aerosol load to surfaces in the
canopy space.
The arrival of African LRT plumes clearly coincides with increased equivalent
black carbon mass concentrations (MBCe) and light scattering
coefficients (σsp), underlining that the LRT aerosols
typically represent mixtures of Saharan dust, biomass burning smoke, and sea
spray (Talbot et al., 1990; Quinn et al., 1996; O'Dowd et al., 2008; Wang et
al., 2016; Aller et al., 2017; Huang and Jaeglé, 2017). The degree of
“smokiness” of the arriving LRT plumes decreases towards the end of the wet
season (April) which is consistent with the decreasing biomass burning
activity in Africa, and simultaneously marks the cleanest periods at the ATTO
site.
The complex emission, transport, and
transformation processes involved in the LRT of African dust into the Amazon
Basin is well represented in a recent modeling study by Wang et al. (2016).
Measured and modeled results of dust mass concentrations are in good
agreement and encourage quantifying regional deposition fluxes of individual
chemical components. Based on these results, we estimated a dust deposition
flux of 5–10 kg ha-1 a-1 in the ATTO region, which is in good
agreement with previous studies (Swap et al., 1992; Yu et al., 2015).
Furthermore, a chemical analysis of aerosol filters with and without LRT
influence confirmed an increase of crustal and sea salt elements during the
LRT events. With this compositional information, we estimated elemental
deposition fluxes in the ATTO region, which is particularly relevant for
those elements that are considered as dust-related nutrients for the rain
forest ecosystem.
Overall, this study provides a comprehensive overview of the physical and
chemical properties of coarse mode aerosols in the Amazon
Basin, highlighting background PBAP
and LRT conditions. The results serve as a basis for further in-depth studies
on the complex coarse mode aerosol composition and cycling as well as its
significance for atmospheric, biogeochemical, and ecological processes.